class TestLowRank(unittest.TestCase):
"""test class for CLowRankCF"""
def setUp(self):
sp.random.seed(1)
self.n = 4
self.rank = 2
self.C = LowRankCov(self.n, self.rank)
self.name = 'lowrank'
self.C.setRandomParams()
def test_grad(self):
def func(x, i):
self.C.setParams(x)
return self.C.K()
def grad(x, i):
self.C.setParams(x)
return self.C.K_grad_i(i)
x0 = self.C.getParams()
err = mcheck_grad(func, grad, x0)
np.testing.assert_almost_equal(err, 0., decimal=6)
def test_param_activation(self):
self.assertEqual(len(self.C.getParams()), 8)
self.C.act_X = False
self.assertEqual(len(self.C.getParams()), 0)
self.C.setParams(np.array([]))
with self.assertRaises(ValueError):
self.C.setParams(np.array([0]))
with self.assertRaises(ValueError):
self.C.K_grad_i(0)
class TestLowRank(unittest.TestCase):
"""test class for CLowRankCF"""
def setUp(self):
sp.random.seed(1)
self.n = 4
self.rank = 2
self.C = LowRankCov(self.n,self.rank)
self.name = 'lowrank'
self.C.setRandomParams()
def test_grad(self):
def func(x, i):
self.C.setParams(x)
return self.C.K()
def grad(x, i):
self.C.setParams(x)
return self.C.K_grad_i(i)
x0 = self.C.getParams()
err = mcheck_grad(func, grad, x0)
np.testing.assert_almost_equal(err, 0., decimal = 6)
def test_param_activation(self):
self.assertEqual(len(self.C.getParams()), 8)
self.C.act_X = False
self.assertEqual(len(self.C.getParams()), 0)
self.C.setParams(np.array([]))
with self.assertRaises(ValueError):
self.C.setParams(np.array([0]))
with self.assertRaises(ValueError):
self.C.K_grad_i(0)
class CategoricalCov(Covariance):
"""
Categorical covariance.
cov_{i,j} depends on the category of i and j only
Main members of the class:
- categories: a vector of length the dimension of the cov matrix with the
categories to which every sample belongs
- cat_cov, a covariance matrix between categories, whose dimensions are determined
by the number of categories -> can be free form or low rank
"""
def __init__(self, categories, rank=None, cat_star=None, jitter= 1e-4):
Covariance.__init__(self)
self.cat = categories
self.jitter = jitter
self.rank = rank
self.cat_star = cat_star
#####################
# properties
#####################
@property
def cat(self):
return self._cat
@property
def rank(self):
return self._rank
@property
def cat_star(self):
return self._cat_star
#####################
# Setters
#####################
@cat.setter
def cat(self, value):
self._cat = value
# initialise related members
self.dim = len(value)
self.unique_cats = np.unique(value)
self.n_cats = len(self.unique_cats)
# initialise int indexed categories
self._i_cat = np.zeros(len(value), dtype=int)
for i in range(self.n_cats):
self._i_cat += (value == self.unique_cats[i])*i
self.clear_all()
self._notify()
@rank.setter
def rank(self, value):
self._rank = value
if value is None:
self.cat_cov = FreeFormCov(self.n_cats, jitter=self.jitter)
else:
assert value <= self.n_cats, 'rank cant be higher than number of categories'
self.cat_cov = LowRankCov(self.n_cats, value)
self.clear_all()
self._notify()
@cat_star.setter
def cat_star(self, value):
self._cat_star = value
if value is None:
self._use_to_predict = False
else:
self._use_to_predict = True
# check that all categories in cat_star are also found in cats
cat_star_uq = np.unique(self.cat_star)
assert all(np.in1d(cat_star_uq, self.unique_cats)), 'all categories used for prediction must be seen during training'
# build the int category vector
# TODO private and change names higher up
self._i_cat_star = np.zeros(len(self.cat_star))
for i in range(self.n_cats):
self._i_cat_star += (self.cat_star == self.unique_cats[i])*i
self.clear_all()
self._notify()
@Covariance.use_to_predict.setter
def use_to_predict(self,value):
if value:
assert self.cat_star is not None, 'set cat_star!'
self._use_to_predict = value
self._notify()
#####################
# Params handling
#####################
def setParams(self, params):
self.cat_cov.setParams(params)
self.clear_cache('K', 'K_grad_i')
self._notify()
def getParams(self):
return self.cat_cov.getParams()
def getInterParams(self):
return self.cat_cov.getInterParams()
def getNumberParams(self):
return self.cat_cov.getNumberParams()
#####################
# cov and gradient
#####################
@cached
def K(self):
R = self.cat_cov.K()
return self.expand(R)
@cached
def K_grad_i(self, i):
R = self.cat_cov.K_grad_i(i)
return self.expand(R)
@cached
def Kcross(self):
R = self.cat_cov.K()
return self.expand_star(R)
# TODO: hessian ? not implemented for lowrank
#####################
# Expanding the category * category matrices
#####################
# for the covariance or its gradient
def expand(self, mat):
R = np.zeros([self.dim, self.dim])
for i in range(self.n_cats):
for j in range(self.n_cats):
tmp_i = (self._i_cat == i)[:, None]
tmp_j = (self._i_cat == j)[None, :]
R += tmp_i.dot(tmp_j) * mat[i,j]
return R
# for the cross covariance
def expand_star(self, mat):
n_star = len(self.cat_star)
R = np.zeros([n_star, self.dim])
for i in range(self.n_cats):
for j in range(self.n_cats):
tmp_i = (self._i_cat_star == i)[:, None]
tmp_j = (self._i_cat == j)[None, :]
R += tmp_i.dot(tmp_j) * mat[i,j]
return R
class CategoricalCov(Covariance):
"""
Categorical covariance.
cov_{i,j} depends on the category of i and j only
Main members of the class:
- categories: a vector of length the dimension of the cov matrix with the
categories to which every sample belongs
- cat_cov, a covariance matrix between categories, whose dimensions are determined
by the number of categories -> can be free form or low rank
"""
def __init__(self, categories, rank=None, cat_star=None, jitter=1e-4):
Covariance.__init__(self)
self.cat = categories
self.jitter = jitter
self.rank = rank
self.cat_star = cat_star
#####################
# properties
#####################
@property
def cat(self):
return self._cat
@property
def rank(self):
return self._rank
@property
def cat_star(self):
return self._cat_star
#####################
# Setters
#####################
@cat.setter
def cat(self, value):
self._cat = value
# initialise related members
self.dim = len(value)
self.unique_cats = np.unique(value)
self.n_cats = len(self.unique_cats)
# initialise int indexed categories
self._i_cat = np.zeros(len(value), dtype=int)
for i in range(self.n_cats):
self._i_cat += (value == self.unique_cats[i]) * i
self.clear_all()
self._notify()
@rank.setter
def rank(self, value):
self._rank = value
if value is None:
self.cat_cov = FreeFormCov(self.n_cats, jitter=self.jitter)
else:
assert value <= self.n_cats, 'rank cant be higher than number of categories'
self.cat_cov = LowRankCov(self.n_cats, value)
self.clear_all()
self._notify()
@cat_star.setter
def cat_star(self, value):
self._cat_star = value
if value is None:
self._use_to_predict = False
else:
self._use_to_predict = True
# check that all categories in cat_star are also found in cats
cat_star_uq = np.unique(self.cat_star)
assert all(
np.in1d(cat_star_uq, self.unique_cats)
), 'all categories used for prediction must be seen during training'
# build the int category vector
# TODO private and change names higher up
self._i_cat_star = np.zeros(len(self.cat_star))
for i in range(self.n_cats):
self._i_cat_star += (self.cat_star == self.unique_cats[i]) * i
self.clear_all()
self._notify()
@Covariance.use_to_predict.setter
def use_to_predict(self, value):
if value:
assert self.cat_star is not None, 'set cat_star!'
self._use_to_predict = value
self._notify()
#####################
# Params handling
#####################
def setParams(self, params):
self.cat_cov.setParams(params)
self.clear_cache('K', 'K_grad_i')
self._notify()
def getParams(self):
return self.cat_cov.getParams()
def getInterParams(self):
return self.cat_cov.getInterParams()
def getNumberParams(self):
return self.cat_cov.getNumberParams()
#####################
# cov and gradient
#####################
@cached
def K(self):
R = self.cat_cov.K()
return self.expand(R)
@cached
def K_grad_i(self, i):
R = self.cat_cov.K_grad_i(i)
return self.expand(R)
@cached
def Kcross(self):
R = self.cat_cov.K()
return self.expand_star(R)
# TODO: hessian ? not implemented for lowrank
#####################
# Expanding the category * category matrices
#####################
# for the covariance or its gradient
def expand(self, mat):
R = np.zeros([self.dim, self.dim])
for i in range(self.n_cats):
for j in range(self.n_cats):
tmp_i = (self._i_cat == i)[:, None]
tmp_j = (self._i_cat == j)[None, :]
R += tmp_i.dot(tmp_j) * mat[i, j]
return R
# for the cross covariance
def expand_star(self, mat):
n_star = len(self.cat_star)
R = np.zeros([n_star, self.dim])
for i in range(self.n_cats):
for j in range(self.n_cats):
tmp_i = (self._i_cat_star == i)[:, None]
tmp_j = (self._i_cat == j)[None, :]
R += tmp_i.dot(tmp_j) * mat[i, j]
return R
